Foreign object recognition for mine conveyor belt iron separators based on transfer learning with EfficientNet by Hailong YANG, Yiping YUAN, Panpan FAN, Lu XIAO, Feiyang ZHAO, Shaoke YUAN

“…This allowed for the stacking and analysis of feature maps at different levels, extracting deep feature signals from the images. …”
Get full text

Simultaneous Estimation of Wrist Joint Angle and Torque During Isokinetic Contraction Based on HD-sEMG by Mingjie Yan, Zhe Chen, Jianmin Li, Jinhua Li, Lizhi Pan

“…To decode these signals, a convolutional neural network (CNN) incorporating the global attention mechanism was established, named global attention convolutional neural network (GACNN). …”
Get full text

Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum by Jiayu Gao, Xuhui Yang, Simo Liu, Yufeng Liu, Xiaofeng Ning

“…In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. …”
Get full text

Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System by Muhenad Bilal, Ranadheer Podishetti, Daniel Grossmann, Markus Bregulla

“…It is compared with conventional diffuse ring illumination to assess its effectiveness in evaluating state-of-the-art convolutional neural networks. This enabled a more targeted investigation of the role of global shape characteristics such as silhouettes versus localized features like the tool face, cutting edges, and delicate geometrical structures under different training strategies. …”
Get full text

Attention-Driven Hybrid Ensemble Approach With Bayesian Optimization for Accurate Energy Forecasting in Jeju Island’s Renewable Energy System by Muhammad Ali Iqbal, Joon-Min Gil, Soo Kyun Kim

“…ABHEF integrates state-of-the-art models—ConvBiLSTM (Convolutional Bidirectional Long Short-Term Memory), ETCN (Enhanced Temporal Convolutional Network), TFT (Temporal Fusion Transformer), and DAT (Dual Attention Transformer)—to capture both short-term fluctuations and long-term trends in energy data. …”
Get full text

Exploration and application of deep learning based wellbore deformation forecasting model by Hui LIU, Guoqiang LI, Xiaojun ZHU, Pengfei ZHANG, Hua CHENG, Jinzheng WANG, Peishuai LI

“…In response to the tilting and damage disasters of deep vertical shafts in thick water-bearing loose layers, the tilting and deformation monitoring of shafts was carried out by taking the deep vertical shaft (800 m) of a mine in Lunan as the research object, studying the spatial and temporal change characteristics of shaft tilting, and analyzing the main influencing factors of shaft tilting; based on this, based on the deep learning theory, four types of deep learning method, namely, recurrent neural network (RNN), long and short-term memory network (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1DCNN), were used. unit (GRU), and one-dimensional convolutional neural network (1DCNN) to construct a wellbore tilt deformation prediction model, and compare the prediction results with the measured values to analyze the accuracy of the wellbore deformation prediction model, validate the reliability of the model, studied overall wellbore and critical area prediction effects, and carry out engineering applications. …”
Get full text

A Novel Parallel Multi-Scale Attention Residual Network for the Fault Diagnosis of a Train Transmission System by Yong Chang, Tengfei Gao, Juanhua Yang, Zongyao Liu, Biao Wang

“…Firstly, multi-scale learning modules (MLMods) with different structures and convolutional kernel sizes are designed by combining a residual neural network (ResNet) and an Inception network, which can automatically learn multi-scale fault information from vibration signals. …”
Get full text

Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach by Tahereh Vasei, Harshil Gediya, Maryam Ravan, Anand Santhanakrishnan, David Mayor, Tony Steffert

“…Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. …”
Get full text

Anomaly-Based Intrusion Detection System for the Internet of Medical Things by Eichie Franklin, Bernardi Pranggono

“…However, there was no single best model in classifying all attack labels, as each model performed differently in terms of different metrics.…”
Get full text

The Role of ChatGPT in Dermatology Diagnostics by Ziad Khamaysi, Mahdi Awwad, Badea Jiryis, Naji Bathish, Jonathan Shapiro

“…Artificial intelligence (AI), especially large language models (LLMs) like ChatGPT, has disrupted different medical disciplines, including dermatology. …”
Get full text

Adjusting U-Net for the aortic abdominal aneurysm CT segmentation case by R.U. Epifanov, N.A. Nikitin, A.A. Rabtsun, L.N. Kurdyukov, A.A. Karpenko, R.I. Mullyadzhanov

“…In this paper, we address the issue of developing of a convolutional neural network for the problem of aneurysm segmentation into three classes and of exploring ways for improving the quality of final segmentation masks. …”
Get full text

Enhanced multiscale plant disease detection with the PYOLO model innovations by Yirong Wang, Yuhao Wang, Jiong Mu, Ghulam Raza Mustafa, Qianqian Wu, Ying Wang, Bi Zhao, Siyue Zhao

“…Additionally, the model’s ability to focus on different parts of the image is improved by redesigning the EC2f structure and dynamically adjusting the convolutional kernel size to better capture features at various scales. …”
Get full text

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data by Juan Pablo Carvajal-Dossman¹, Laura Guio, Danilo García-Orjuela, Jennifer J. Guzmán-Porras, Kelly Garces, Andres Naranjo, Silvia Juliana Maradei-Anaya, Jorge Duitama

“…However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. …”
Get full text

Study of the Current–Voltage Characteristics of Membrane Systems Using Neural Networks by Evgenia Kirillova, Anna Kovalenko, Makhamet Urtenov

“…During this work, several different neural network architectures were developed and tested. …”
Get full text

A semantic‐based method for analysing unknown malicious behaviours via hyper‐spherical variational auto‐encoders by Yi‐feng Wang, Yuan‐bo Guo, Chen Fang

“…The authors further use a Graph Convolutional Network (GCN) to reduce the impact of different user behaviour patterns before projection. …”
Get full text

A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies by Walaa Gamaleldin, Osama Attayyib, Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Ruixing Ming

“…The model integrates long short-term memory (LSTM) networks and convolutional neural networks (CNN) to analyze historical claim data and identify emerging risk trends. …”
Get full text

Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network by Chen Zhou, Fan Zhang, Edric John Cruz Nacpil, Zheng Wang, Fei-Xiang Xu

“…The proposed prediction method also possesses adaptability to different driver behaviors.…”
Get full text

4D hypercomplex-valued neural network in multivariate time series forecasting by Radosław Kycia, Agnieszka Niemczynowicz

“…Abstract The goal of this paper is to test three classes of neural network (NN) architectures based on four-dimensional (4D) hypercomplex algebras for multivariate time series forecasting. We evaluate different architectures, varying the input layers to include convolutional, Long Short-Term Memory (LSTM), or dense hypercomplex layers for 4D algebras. …”
Get full text

Fusion of Deep Features of Wavelet Transform for Wildfire Detection by Akbar Asgharzadeh-Bonab, Salar Ghamati, Farid Ahmadi, Hashem Kalbkhani

“…Forests uniquely deliver different vital resources, particularly oxygen and carbon dioxide purification. …”
Get full text

Advanced investing with deep learning for risk-aligned portfolio optimization. by Minh Duc Nguyen

“…This study introduces a deep learning-based framework for portfolio optimization tailored to different investor risk preferences. We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). …”
Get full text